Learning Probability Distributions in Macroeconomics and Finance

Jozef Baruník and Luboš Hanus

Papers from arXiv.org

Abstract: We propose a deep learning approach to probabilistic forecasting of macroeconomic and financial time series. Being able to learn complex patterns from a data rich environment, our approach is useful for a decision making that depends on uncertainty of large number of economic outcomes. Specifically, it is informative to agents facing asymmetric dependence of their loss on outcomes from possibly non-Gaussian and non-linear variables. We show the usefulness of the proposed approach on the two distinct datasets where a machine learns the pattern from data. First, we construct macroeconomic fan charts that reflect information from high-dimensional data set. Second, we illustrate gains in prediction of stock return distributions which are heavy tailed, asymmetric and suffer from low signal-to-noise ratio.

Date: 2022-04
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-for, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2204.06848

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